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Background There were 28.76 million stroke patients in China, with 3.94 million new strokes annually. Around 70% of survivors live with varying degrees of permanent disability, placing a heavy burden on families and society. Stroke patients have a complex and high need for continuing care services after discharge from the hospital. The study was to evaluate the effectiveness of the continuing care program on the activity of daily life, the degree of disability, stroke recurrence and readmission in patients with stroke, based on the long-term care insurance policy. Methods Eighty-eight stroke patients were recruited from February 2021 to January 2022. They were allocated to either intervention group (N = 44) or control group (N = 44) based on whether the long-term care insurance application was successful. The intervention consisted of discharge education, 3-month in-home care and remote counseling. The activity of daily life, disability, recurrence and readmission of patients were evaluated between the groups before the intervention, at 1, 2, 3 and 6 months after discharge. Results There were no statistically significant differences between the groups in clinical characteristics at baseline. We detected significant interaction effects between time and group (p χ2 = 6.040, p = 0.014; χ2 = 7.216, p = 0.007) at 1 and 2 months after discharge, but no significant difference was found at 3 and 6 months after discharge. At follow-up, there was no significant difference between groups regarding stroke recurrence rates (p > 0.05). Conclusions This study demonstrates the positive effects of a continuing care program based on the long-term care insurance policy on activity of daily life and disability in people with stroke. The effectiveness and feasibility of this intervention program suggest that its application should be promoted to promote the care and rehabilitation of stroke patients. Trail registration: The trail was not registered as a quasi-experimental trail.

The relationship between house prices and the economy is an important motivating factor for predicting house prices. Housing price trends are not only the concern of buyers and sellers, but it also indicates the current economic situation. Therefore, it is important to predict housing prices without bias to help both the buyers and sellers make their decisions. This project uses an open source dataset, which include 20 explanatory features and 21,613 entries of housing sales in King County, USA. We compare different feature selection methods and feature extraction algorithm with Support Vector Regression (SVR) to predict the house prices in King County, USA. The feature selection methods used in the experiments include Recursive Feature Elimination (RFE), Lasso, Ridge, and Random Forest Selector. The feature extraction method in this work is Principal Component Analysis (PCA). After applying different feature reduction methods, a regression model using SVR was built. With log transformation, feature reduction, and parameter tuning, the price prediction accuracy increased from 0.65 to 0.86. The lowest MSE is 0.04. The experimental results show there is no difference in performance between PCA-SVR and feature selections-SVR in predicting housing prices in King County, USA. The benefit of applying feature reductions is that it helps us to pick the more important features, so we will not over-fit the model with too many features.